Rail transit geological hidden danger troubleshooting system

Abstract

The invention discloses a rail transit geological hidden danger checking system, and relates to the technical field of rail transit. The rail transit geological hidden danger troubleshooting system comprises a data acquisition layer, and the data acquisition layer is connected with a data processing and analyzing layer. The system provided by the invention can realize all-weather real-time monitoring by utilizing a dense sensor network, provides multi-dimensional and fully-covered geological change monitoring by integrating geological, meteorological and traffic multi-source data, and can automatically analyze a large amount of data and accurately identify a geological risk trend by virtue of a machine learning and data fusion technology, so that the real-time monitoring of the geological risk trend is realized. And timely and clear warning information is provided for related personnel through a visual visualization tool and a multi-channel early warning mechanism, automatic and intelligent processing greatly reduces manpower requirements, improves efficiency, ensures safe operation of rail transit, and meanwhile, convenient data visualization and comprehensive analysis capability are achieved, and the system is suitable for popularization and application. And a user can easily understand and deal with potential safety hazards.

Description

Technical Field

[0001] The present invention relates to the technical field of rail transit, and in particular to a system for detecting geological hazards in rail transit. Background Art

[0002] Rail transit refers to a mode of transportation that transports passengers or freight by rail, including subways, light rails, railways, high-speed rails, trams and other forms. As an important part of urban infrastructure, rail transit has become a key means to alleviate urban traffic congestion, reduce air pollution and promote economic development with its advantages of high efficiency, speed and large capacity. However, since rail transit often passes through complex geological environments, its stability and safety may be affected by geological factors such as ground subsidence, landslides, earthquakes, etc. These geological hazards may cause track deformation, damage to infrastructure, and even cause serious accidents. Therefore, it is very important to investigate the geological hazards of rail transit.

[0003] At present, the investigation of geological hazards in rail transit usually adopts several traditional methods, including geological drilling and sampling analysis, to obtain underground rock and soil information to evaluate the geological conditions along the line. Equipment such as GPS and total station can also be used to regularly monitor surface displacement and settlement to detect surface changes. In addition, radar wave and sound wave detection technology are used to scan underground structures to detect possible voids or unstable areas. In addition, experts and engineering technicians will also conduct regular manual inspections along the track to identify potential geological problems. However, these traditional methods often rely on regular data collection and analysis, and cannot achieve real-time monitoring and early warning. At the same time, due to the limitations of equipment use and cost, it is often difficult to achieve comprehensive coverage. The data source is relatively single, and lacks the ability to comprehensively analyze multiple data types. At the same time, a large amount of manpower is required for on-site investigation and data processing, and the efficiency is relatively low. Summary of the invention

[0004] In view of the defects in the prior art, the present invention provides a rail transit geological hazard inspection system, comprising a data acquisition layer, the data acquisition layer is connected to a data processing and analysis layer, the data processing and analysis layer is connected to an interaction and response layer, and the interaction and response layer is connected to a response and feedback layer;

[0005] The data collection layer includes sensor networks, natural monitoring systems and community participation platforms; the data processing and analysis layer includes data storage architecture, intelligent analysis and evaluation, and natural phenomenon simulation; the interaction and response layer includes user interfaces and dashboards, early warning and notification systems, and expert exchange platforms; the response and feedback layer includes on-site emergency response, continuous improvement and feedback, and user education and participation.

[0006] Preferably, the sensor network includes geological sensors, environmental sensors, and moisture monitoring sensors. The geological sensors are used to monitor seismic activity, surface tilt and strain state. The geological sensors are arranged in key geological risk areas along the track to collect geological stress data in real time. The environmental sensors are used to monitor temperature, humidity and air pressure environmental parameters. The environmental sensors are installed at certain intervals along the track to identify the impact of potential climate change on geology. The moisture monitoring sensors identify potential landslide risks by detecting soil moisture and groundwater level. The moisture monitoring sensors are installed in areas prone to water accumulation and high humidity to provide timely feedback on water level changes.

[0007] Preferably, the natural monitoring system includes plant monitoring and animal behavior perception. Plant monitoring indirectly reflects geological changes by monitoring the health status of plants. Plant status sensors are installed to record chlorophyll concentration and vegetation coverage. Animal behavior perception is used to monitor abnormal animal activities as an early warning of natural changes. Infrared cameras and sound sensors can be used to record the passage of animals and behavior patterns. Earthquake prediction is performed based on the results of plant monitoring and animal behavior perception.

[0008] Preferably, the community participation platform allows the public to report geological anomalies, such as soil subsidence and cracks, through a mobile application. Users can upload pictures, videos and locate the location, and the information is automatically imported into the system database. Reports from residents and workers along the route are collected and organized, and multi-source information is integrated into the analysis platform to enrich the data set.

[0009] Preferably, the data storage architecture is to use PostgreSQL or NoSQL to establish a cloud database for storing sensor data and community reports, the intelligent analysis and evaluation is to use convolutional neural networks for image recognition, and random forests to process and analyze geological, climate and biological monitoring data to identify risk trends, and the natural phenomenon simulation is to use modeling software for physical simulation, combined with historical data for calibration, to simulate geological disasters (landslides, mudslides) for risk pre-assessment.

[0010] Preferably, the user interface and dashboard are for real-time data display, including charts, geographic information system (GIS) maps and alarm information display; the warning and notification system is for dynamically adjusting the alarm level according to model analysis and sensor trigger data, setting multi-level warnings from attention to emergency, and then pushing warnings to relevant personnel via SMS, email and mobile applications to achieve multi-channel real-time alarms; the expert communication platform is for providing analysis results and simulation suggestions, and making decisions through expert evaluation.

[0011] Preferably, the on-site emergency response is to establish dedicated channels for emergency communications and resource allocation, and coordinate geological disaster response with local emergency management and transportation operations departments; the continuous improvement and feedback is to integrate user feedback into the model optimization process, and then regularly collect and analyze action data; the user education and participation is to publish information through websites and applications, combine online courses and forums, provide educational materials and training, and enhance public awareness of geological hazards.

[0012] The beneficial effects of the present invention are embodied in:

[0013] The advantage of the system of the present invention is that it can realize real-time monitoring along the rail transit line. By deploying a sensor network, it can realize 24-hour uninterrupted data collection and analysis and provide real-time warning. In addition, the system has the ability to fully cover and can conduct all-round monitoring of geological changes from multiple dimensions in the entire monitoring area. Through multi-source data fusion, geological sensors, environmental data and community reports are integrated to improve the accuracy of analysis. By using machine learning and big data technology, the system can autonomously identify potential geological risks, shorten response time, and quickly notify relevant personnel through convenient visualization tools and multi-channel warning functions, thereby greatly reducing dependence on manpower and improving overall efficiency and safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following is a brief introduction to the drawings required for the specific embodiments or the description of the prior art. In all the drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, the elements or parts are not necessarily drawn according to the actual scale.

[0015] Figure 1 It is a schematic diagram of the system framework flow of the present invention;

[0016] Figure 2 It is a schematic diagram showing the visual circuit of the system of the present invention;

[0017] Figure 3 A schematic diagram showing a visual site display of the system of the present invention;

[0018] Figure 4 A schematic diagram showing the visual inspection points of the system of the present invention;

[0019] Figure 5 It is a schematic diagram showing the details of the system visualization checkpoints of the present invention;

[0020] Figure 6 This is a schematic diagram showing the types of system visualization checkpoints of the present invention;

[0021] Figure 7It is a schematic diagram showing the system visual risk level of the present invention. DETAILED DESCRIPTION

[0022] The following embodiments of the technical solution of the present invention are described in detail in conjunction with the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and are therefore only used as examples, and cannot be used to limit the protection scope of the present invention.

[0023] It should be noted that, unless otherwise specified, the technical terms or scientific terms used in this application should have the common meanings understood by those skilled in the art to which the present invention belongs.

[0024] like Figure 1-7 As shown, an embodiment of the present invention provides a rail transit geological hazard inspection system, including a data acquisition layer, the data acquisition layer is connected to a data processing and analysis layer, the data processing and analysis layer is connected to an interaction and response layer, and the interaction and response layer is connected to a response and feedback layer;

[0025] The data collection layer includes sensor networks, natural monitoring systems and community participation platforms; the data processing and analysis layer includes data storage architecture, intelligent analysis and evaluation, and natural phenomenon simulation; the interaction and response layer includes user interface and dashboard, early warning and notification system, and expert exchange platform; the response and feedback layer includes on-site emergency response, continuous improvement and feedback, and user education and participation.

[0026] The sensor network includes geological sensors, environmental sensors, and moisture monitoring sensors. Geological sensors are used to monitor seismic activity, surface tilt and strain status. Geological sensors are installed in key geological risk areas along the track to collect geological stress data in real time. Environmental sensors are used to monitor temperature, humidity and air pressure environmental parameters. Environmental sensors are installed at certain intervals along the track to identify the impact of potential climate change on geology. Moisture monitoring sensors identify potential landslide risks by detecting soil moisture and groundwater levels. Moisture monitoring sensors are installed in areas prone to water accumulation and high humidity to provide timely feedback on water level changes.

[0027] The natural monitoring system includes plant monitoring and animal behavior perception. Plant monitoring indirectly reflects geological changes by monitoring the health of plants. Plant status sensors are installed to record chlorophyll concentration and vegetation coverage. Animal behavior perception is used to monitor abnormal animal activities as an early warning of natural changes. Infrared cameras and sound sensors can be used to record the passage of animals and behavior patterns. Earthquake predictions are made based on the results of plant monitoring and animal behavior perception.

[0028] The community participation platform allows the public to report geological anomalies such as soil subsidence and cracks through a mobile application. Users can upload pictures, videos and locate the information, which will be automatically imported into the system database. Reports from residents and workers along the route will be collected and organized, and multi-source information will be integrated into the analysis platform to enrich the data set.

[0029] The data storage architecture uses PostgreSQL or NoSQL to establish a cloud database for storing sensor data and community reports. Intelligent analysis and evaluation uses convolutional neural networks for image recognition and random forests to process and analyze geological, climate and biological monitoring data to identify risk trends. Natural phenomenon simulation uses modeling software for physical simulation, combined with historical data for calibration, to simulate geological disasters for risk pre-assessment.

[0030] The user interface and dashboard are used to display real-time data, including charts, geographic information system maps and alarm information. The early warning and notification system dynamically adjusts the alarm level based on model analysis and sensor trigger data, sets multi-level early warnings, from attention to emergency, and then pushes the warnings to relevant personnel via SMS, email and mobile applications to achieve multi-channel real-time alarms. The expert communication platform provides analysis results and simulation suggestions, and makes decisions through expert evaluation.

[0031] Field emergency response is to establish dedicated channels for emergency communications and resource allocation, and coordinate geological hazard response with local emergency management and transportation operations departments. Continuous improvement and feedback is to integrate user feedback into the model optimization process, and then regularly collect and analyze action data. User education and participation is to publish information through websites and applications, combine online courses and forums, provide educational materials and training, and enhance public awareness of geological hazards.

[0032] Comprehensive monitoring and management of geological hazards in rail transit are carried out by building a comprehensive sensor network and natural monitoring system. The sensor network includes geological sensors such as vibration sensors, tilt sensors and ground strain gauges, as well as environmental sensors such as temperature and humidity sensors and atmospheric pressure gauges, which are deployed every 500 meters along the railway line. In addition, there are soil moisture sensors to detect groundwater levels and water saturation. In the natural monitoring system, specific plant status sensors are installed in the vegetation around the track to record plant health parameters in real time, while infrared cameras and sound recording devices are used to observe and record animal behavior. In the community participation reporting mechanism, users upload photos, videos and text descriptions of geological anomalies, and the report source is accurately marked through location positioning technology and integrated into the central database. At the data processing and analysis layer, AWS RDS or Google Cloud Spanner is used to manage data reported by sensors and residents, and JSON / XML format is used for data exchange. Intelligent analysis uses deep learning models (such as convolutional neural networks) to analyze video data, identify abnormal animal behavior, and process multivariate sensor data through random forest algorithms to detect geological anomalies. Large-scale data processing uses Apache Hadoop and Spark for batch processing and real-time data stream processing. At the same time, natural phenomena are simulated through Tsunami and Hydro-Geological Simulation Models, and the models and simulation results are visualized with the help of the Unity3D platform. The early warning system has real-time alarm functions, setting multiple levels such as yellow alert (attention), orange alert (warning) and red alert (emergency), and push multi-channel notifications through SMS, email and mobile applications. The system also has integrated SaaS tools for experts to discuss decisions in real time and provide rule-based automated decision-making suggestions. The response and feedback layer emphasizes the on-site emergency response mechanism, cooperates with traffic management and emergency service departments, and conducts scenario-based drills regularly to improve response capabilities. Continuous optimization is achieved by automatically absorbing actual combat action data and feeding it back to the system database, and an iterative development method is used to improve system performance. Users can improve their understanding of geological hazards through the knowledge sharing platform.

[0033] like Figure 2 As shown in the figure, in this system, clicking on the "Line" in the upper left corner will select the "Line" in all subway lines, and the track shape of each subway line will be displayed on the map, such as Figure 3 As shown in the figure, click and check the "Site" in the upper left corner to select the "Site" of all subway lines, and the map will show the stations and their names included in each subway line. Figure 4 As shown in the figure, click and check the "Checkpoint" on the upper left to select the "Checkpoint" of all subway lines (including ground checkpoints and underground checkpoints), and the checkpoints and their names of each subway line will be displayed on the map. Figure 5As shown in the figure, when Line 3 is checked on the left, the checkpoint information of Line 3 is displayed on the right. Figure 6 As shown in the figure, there are five types of screening items for the inspection point type: above-ground slope point, above-ground inspection point, underground tunnel inspection point, underground inspection point, and geological radar point. Figure 7 As shown, the risk level is divided into 4 levels: level 1, level 2, level 3, and level 4.

[0034] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein by equivalents. These modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be included in the scope of the claims and specification of the present invention.

Claims

1. A rail transit geological hazard inspection system, including a data acquisition layer, characterized in that: The data collection layer is connected to the data processing and analysis layer, the data processing and analysis layer is connected to the interaction and response layer, and the interaction and response layer is connected to the response and feedback layer; The data collection layer includes sensor networks, natural monitoring systems and community participation platforms; the data processing and analysis layer includes data storage architecture, intelligent analysis and evaluation, and natural phenomenon simulation; the interaction and response layer includes user interfaces and dashboards, early warning and notification systems, and expert exchange platforms; the response and feedback layer includes on-site emergency response, continuous improvement and feedback, and user education and participation.

2. A rail transit geological hazard detection system according to claim 1, characterized in that: The sensor network includes geological sensors, environmental sensors, and moisture monitoring sensors. The geological sensors are used to monitor seismic activity, surface tilt and strain status. The geological sensors are set in key geological risk areas along the track to collect geological stress data in real time. The environmental sensors are used to monitor temperature, humidity and air pressure environmental parameters. The environmental sensors are installed at certain intervals along the track to identify the impact of potential climate change on geology. The moisture monitoring sensors identify potential landslide risks by detecting soil moisture and groundwater levels. The moisture monitoring sensors are installed in areas prone to water accumulation and high humidity to provide timely feedback on water level changes.

3. A rail transit geological hazard detection system according to claim 1, characterized in that: The natural monitoring system includes plant monitoring and animal behavior perception. Plant monitoring indirectly reflects geological changes by monitoring the health status of plants. Plant status sensors are installed to record chlorophyll concentration and vegetation coverage. Animal behavior perception is used to monitor abnormal animal activities as an early warning of natural changes. Infrared cameras and sound sensors can be used to record the passage and behavior patterns of animals. Earthquake prediction is carried out based on the results of plant monitoring and animal behavior perception.

4. A rail transit geological hazard detection system according to claim 1, characterized in that: The community participation platform allows the public to report geological anomalies such as soil subsidence and cracks through a mobile application. Users can upload pictures, videos and locate their locations. The information is automatically imported into the system database, which collects and organizes reports from residents and workers along the route, integrates multi-source information into the analysis platform, and enriches the data set.

5. A rail transit geological hazard detection system according to claim 1, characterized in that: The data storage architecture is to use PostgreSQL or NoSQL to establish a cloud database for storing sensor data and community reports. The intelligent analysis and evaluation is to use convolutional neural networks for image recognition and random forests to process and analyze geological, climate and biological monitoring data to identify risk trends. The natural phenomenon simulation is to use modeling software for physical simulation, combined with historical data for calibration, to simulate geological disasters for risk pre-assessment.

6. A rail transit geological hazard detection system according to claim 1, characterized in that: The user interface and dashboard are used to display real-time data, including charts, geographic information system maps and alarm information. The early warning and notification system dynamically adjusts the alarm level based on model analysis and sensor trigger data, sets multi-level early warnings, from attention to emergency, and then pushes the warnings to relevant personnel via text messages, emails and mobile applications to achieve multi-channel real-time alarms. The expert communication platform provides analysis results and simulation suggestions, and makes decisions through expert evaluation.

7. A rail transit geological hazard detection system according to claim 1, characterized in that: The field emergency response is to establish dedicated channels for emergency communications and resource allocation, and coordinate geological disaster response with local emergency management and transportation operations departments. The continuous improvement and feedback is to integrate user feedback into the model optimization process, and then regularly collect and analyze action data. The user education and participation is to publish information through websites and applications, combine online courses and forums, provide educational materials and training, and enhance public awareness of geological hazards.

Images